if u have the materials for this project can u send please sent it to dis id

[email protected]

its urgent.....and i also needed some guidance on dis project......please

thanks

Anna ....etc

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code for color extraction that is color moments with mean and skweness
....etc

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ABSTRACT
Meshes are dominantly used to represent 3D models as they fit well with graphics rendering hardware. Features such as volume, moments, and Fourier transform coeffi- cients need to be calculated from the mesh representation efficiently. In this paper, we propose an algorithm to cal- culate these features without transforming the mesh into other representations such as the volumetric representa- tion. To calculate a feature for a mesh, we show that we can first compute it for each elementary shape such as a triangle or a tetrahedron, ....etc

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This article describes about modified band-pass filtered MFCC.Improved noise robustness is obtained by varying the time constants in each channel and adaptive gain compression of the envelope in each channel.This article also presents speech discrimination and
recognition results.
Mel-frequency cepstral coefficients (MFCCs),are not very robust for noise.Reasons include-the number of samples used to represent each frame is small compared to the original sampled waveform in most audio feature extraction processes.FFT followed by grouping int ....etc

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Abstract
This paper demonstrates some consideration results
on feature extraction and selection for handwritten
Japanese Hiragana characters using independent
component analysis (ICA). In some ICA
algorithms where whitening of input signals is introduced
as preprocessing, one can consider that
the process of feature extraction using ICA consists
of two types of transformations: one is the transformation
from an input image to its principal components
(PCs), and the other is the transformation
from PCs to independent component ....etc

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AN FPGA-BASED ARCHITECTURE FOR REAL TIME IMAGE FEATURE EXTRACTION
Real-time image pattern recognition is a challenging task which involves image processing, feature extraction and pattern classification. It applies to a wide range of applications including multimedia , military and medical ones. Its high computational requirements force systems to use very expensive clusters, custom VLSI designs or even both. These approaches suffer from various disadvantages, such as high cost and long development times. Rec ....etc

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please send project report of neural network based handwritten character recognition without feature extraction ....etc

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Absract
This paper proposes a novel feature extractionscheme for audio fingerprinting using discretewavelet transform (DWT). The proposed schemereduces the granularity, i.e., the minimal length ofaudio, needed for identification in an audiofingerprinting system. The scheme first decomposesthe video frame into sub-bands by DWT
The proposed scheme hastwo advantages:
(1) it needs smaller fingerprintgranularity than other previous work;
(2) it is notonly reliable but also robust against various signal
degradations according to the ex ....etc

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Abstract
Edge detection is a basic and important subject in computer vision and image processing. In this paper we discuss several digital image processing techniques applied in edge feature extraction. Firstly, wavelet transform is used to remove noises from the image collected. Secondly, some edge detection operators such as Differential edge detection, Log edge detection, canny edge detection and Binary morphology are analyzed. And then according to the simulation results, the advantages and disadvantages of these edge detection op ....etc

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Summary
This paper proposes a new approach investigating the applicationof moment method to evaluate a set of candidate features and toselect an informative subset to be used as input data for a neuralnetwork classifier. The first step (pre-processing) of proposedmethod takes into account the discriminative properties ofinvariant krawtchouk moments. The second step (recognition) isachieved by using multilayer feedforward neural network(MFNN) as a classifier with the stochastic back propagation as alearning algorithm. Finite vectors obt ....etc

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